Germany: LK Hof (Bayern)¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")

Notebook executed on: 07/03/2023 10:47:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hof", weeks=5);
2023-03-07T10:47:58.392236 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 96.9 LK Hof, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Hof cases daily growth factor Germany-LK Hof cases daily growth factor (rolling mean) Germany-LK Hof estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Hof deaths daily growth factor Germany-LK Hof deaths daily growth factor (rolling mean) Germany-LK Hof estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Hof doubling time cases (rolling mean) 0.00 18.78 37.56 daily change Germany-LK Hof new cases (rolling 7d mean) Germany-LK Hof new cases 0.000 0.235 0.470 0.704 0.939 daily change Germany-LK Hof new deaths (rolling 7d mean) Germany-LK Hof new deaths 0.000 0.242 0.484 0.726 0.968
In [4]:
overview(country="Germany", subregion="LK Hof");
2023-03-07T10:48:13.116599 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 96.9 LK Hof, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 600 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Hof cases daily growth factor Germany-LK Hof cases daily growth factor (rolling mean) Germany-LK Hof estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Hof deaths daily growth factor Germany-LK Hof deaths daily growth factor (rolling mean) Germany-LK Hof estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 cases doubling time [days] Germany-LK Hof doubling time cases (rolling mean) Germany-LK Hof doubling time deaths (rolling mean) 0.0 187.8 375.6 563.4 daily change Germany-LK Hof new cases (rolling 7d mean) Germany-LK Hof new cases 0.00 2.35 4.70 7.04 9.39 daily change Germany-LK Hof new deaths (rolling 7d mean) Germany-LK Hof new deaths 0.0 99.1 198.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hof", dates="2020-03-15:");
2023-03-07T10:50:37.139901 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Hof LK Hof Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Hof Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hof")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hof")
print(f'Population of country="Germany", subregion="LK Hof": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table

Population of country="Germany", subregion="LK Hof": 93907 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 49058 11 316 0
2023-03-03 49047 10 316 0
2023-03-02 49037 15 316 0
2023-03-01 49022 32 316 0
2023-02-28 48990 23 316 0
2023-02-27 48967 40 316 0
2023-02-24 48927 16 316 0
2023-02-23 48911 30 316 0
2023-02-22 48881 33 316 0
2023-02-21 48848 34 316 0
2023-02-20 48814 54 316 1
2023-02-17 48760 29 315 0
2023-02-16 48731 23 315 0
2023-02-15 48708 37 315 1
2023-02-14 48671 29 314 0
2023-02-13 48642 28 314 1
2023-02-10 48614 20 313 0
2023-02-09 48594 19 313 0
2023-02-08 48575 17 313 1
2023-02-07 48558 21 312 0
2023-02-06 48537 31 312 0
2023-02-03 48506 15 312 0
2023-02-02 48491 7 312 0
2023-02-01 48484 23 312 0
2023-01-31 48461 22 312 0
2023-01-30 48439 18 312 0
2023-01-27 48421 14 312 0
2023-01-26 48407 17 312 0
2023-01-25 48390 19 312 0
2023-01-24 48371 22 312 0
2023-01-23 48349 26 312 0
2023-01-20 48323 13 312 1
2023-01-19 48310 10 311 0
2023-01-18 48300 7 311 0
2023-01-17 48293 17 311 1
2023-01-16 48276 11 310 0
2023-01-13 48265 7 310 0
2023-01-12 48258 6 310 0
2023-01-11 48252 21 310 1
2023-01-10 48231 19 309 0
2023-01-09 48212 20 309 0
2023-01-05 48192 13 309 0
2023-01-04 48179 32 309 0
2023-01-03 48147 25 309 0
2023-01-02 48122 38 309 1
2022-12-30 48084 20 308 0
2022-12-29 48064 16 308 0
2022-12-28 48048 37 308 0
2022-12-27 48011 65 308 0
2022-12-23 47946 15 308 0
2022-12-22 47931 22 308 0
2022-12-21 47909 32 308 0
2022-12-20 47877 24 308 1
2022-12-19 47853 32 307 0
2022-12-16 47821 17 307 0
2022-12-15 47804 22 307 0
2022-12-14 47782 24 307 0
2022-12-13 47758 26 307 0
2022-12-12 47732 26 307 0
2022-12-09 47706 23 307 1
2022-12-08 47683 24 306 0
2022-12-07 47659 36 306 1
2022-12-06 47623 19 305 0
2022-12-05 47604 40 305 0
2022-12-02 47564 15 305 0
2022-12-01 47549 12 305 0
2022-11-30 47537 21 305 0
2022-11-29 47516 19 305 0
2022-11-28 47497 27 305 1
2022-11-25 47470 11 304 0
2022-11-24 47459 23 304 1
2022-11-23 47436 30 303 0
2022-11-22 47406 24 303 0
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2022-10-24 46377 143 299 1
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2022-10-20 46150 120 298 1
2022-10-19 46030 147 297 0
2022-10-18 45883 137 297 0
2022-10-17 45746 228 297 1
2022-10-14 45518 79 296 1
2022-10-13 45439 108 295 0
2022-10-12 45331 197 295 2
2022-10-11 45134 134 293 1
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2022-10-07 44761 156 291 2
2022-10-06 44605 195 289 0
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2022-10-04 44300 202 289 2
2022-09-30 44098 67 287 0
2022-09-29 44031 78 287 1
2022-09-28 43953 126 286 0
2022-09-27 43827 83 286 0
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2022-09-23 43618 47 285 0
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Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)
In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")

Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")

Notebook execution took: 0:03:20.423142